Download Free Selected Topics In Medical Artificial Intelligence Book in PDF and EPUB Free Download. You can read online Selected Topics In Medical Artificial Intelligence and write the review.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Computer technology has impacted the practice of medicine in dramatic ways. Imaging techniques provide noninvasive tools which alter the di agnostic process. Sophisticated monitoring equipment presents new levels of detail for both patient management and research. In most of these tech nology applications, the computer is embedded in the device; its presence is transparent to the user. There is also a growing number of applications in which the health care provider directly interacts with a computer. In many cases, these appli cations are limited to administrative functions, e.g., office practice man agement, location of hospital patients, appointments, and scheduling. Nevertheless, there also are instances of patient care functions such as results reporting, decision support, surveillance, and reminders. This series, Computers and Medicine, will focus upon the direct use of information systems as it relates to the medical community. After twenty-five years of experimentation and experience, there are many tested applications which can be implemented economically using the current generation of computers. Moreover, the falling cost of computers suggests that there will be even more extensive use in the near future. Yet there is a gap between current practice and the state-of-the-art.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
At present one of the main obstacles to a broader application of expert systems is the lack of a theory to tell us which problem-solving methods areavailable for a given problem class. Such a theory could lead to significant progress in the following central aims of the expert system technique: - Evaluating the technical feasibility of expert system projects: This depends on whether there is a suitable problem-solving method, and if possible a corresponding tool, for the given problem class. - Simplifying knowledge acquisition and maintenance: The problem-solving methods provide direct assistance as interpretation models in knowledge acquisition. Also, they make possible the development of problem-specific expert system tools with graphical knowledge acquisition components, which can be used even by experts without programming experience. - Making use of expert systems as a knowledge medium: The structured knowledge in expert systems can be used not only for problem solving but also for knowledge communication and tutorial purposes. With such a theory in mind, this book provides a systematic introduction to expert systems. It describes the basic knowledge representations and the present situation with regard tothe identification, realization, and integration of problem-solving methods for the main problem classes of expert systems: classification (diagnostics), construction, and simulation.
Enhanced, more reliable, and better understood than in the past, artificial intelligence (AI) systems can make providing healthcare more accurate, affordable, accessible, consistent, and efficient. However, AI technologies have not been as well integrated into medicine as predicted. In order to succeed, medical and computational scientists must develop hybrid systems that can effectively and efficiently integrate the experience of medical care professionals with capabilities of AI systems. After providing a general overview of artificial intelligence concepts, tools, and techniques, Medical Applications of Artificial Intelligence reviews the research, focusing on state-of-the-art projects in the field. The book captures the breadth and depth of the medical applications of artificial intelligence, exploring new developments and persistent challenges.
This comprehensive book focuses on various aspects of artificial intelligence in dentistry, assisting dentists, specialists, and scientists in advancing their understanding, knowledge, training, and expertise in this field of artificial intelligence. Readers will learn about AI-supported pathways for the diagnosis and treatment of dental caries, periodontal bone loss, impacted teeth, periapical lesions, crown, and root fractures, working length determination, and detecting root and canal morphology, TMJ disorders, detection of obstructive sleep apnea, oral mucosal lesions, and many more. Prediction tasks include the estimation of retreatment needs and third molar eruption. Critical information on applications of AI in the field of Oral and Maxillofacial Radiology, Implants, Endodontics, Prosthodontics, Restorative dentistry, Oral surgery, Periodontics, and Orthodontics. Gain valuable insight into studies applying machine learning based on Machine Learning (ML), Deep Learning (DL), and Artificial Neural Networks (ANN). Explore the technical aspects and medical applications of AI in dentistry. Additionally, discover cutting-edge topics like 3D and bioprinting applications of AI and its integration into dental education. All the chapters provide thorough, evidence-based data on AI and its implications in oral health, bridging the gap between knowledge and practical application. The book explains the advantages, disadvantages, and limitations of AI in dental health. Delve into the medico-legal aspects of AI to navigate this cutting-edge landscape responsibly. Learn about applications of Machine Learning and Artificial Intelligence in the Covid-19 Pandemic. Extensive information on deep learning in image processing, including various types of neural networks, image segmentation, enhancement, reconstruction, and registration. This book concludes with an exploration of AI's exciting potential and future perspectives in the dental field, paving the way for a new era of oral healthcare. Don't miss out on this unique resource for AI in Dentistry, which empowers you to stay at the forefront of innovation and embrace the AI revolution in Dentistry. Be prepared for the future of dentistry.
Title Page -- TABLE OF CONTENTS -- Introduction -- Part A: Methodological Aspects -- Evaluation of Automatic Health Information Systems What and How? -- Technology Assessment in Medical and Health Care Informatics: A Clarification of the Concept -- Supporting System Development with Technology Assessment -- The Conceptionof a Medical Computer System -- Verification and Validation -- Case Acquisition for Knowledge-Based Decision Support System Validation -- Approaches to Experimental Design -- Analysis of Costs of Information Systems -- Measuring Effects -- Methods for Data Acquisition -- From Assessment to Decision-Making -- Technology Assessment for Decision-Making in the Field of Informatics in Medicine and Health Care -- Part B: Examples from AIM Projects -- The Impact of Clinical Pilot Projects in R & D Programmes Supported by the EU -- Assessment and Evaluation of Knowledge-Based Expert Systems for Medical Diagnosis -- Evaluation in the TELEGASTRO-Project -- The KANDID Way to ESTEEM -- On the Evaluation of System Integration -- Protocol for the Clinical Functionality Assessment of a Workstation for Stereotactic Neurosurgery -- SAMMIE Software Applied to MultiModal Images and Education -- Technology Assessment in theEurIpacs Project -- Assessment of Workstations and PACS in AIM: The Experience of the MILORD Project -- Part C: Literature Overview -- Overview of Published Assessment and Evaluation Studies -- Literature on Assessment of Information Technology and Medical KBS Evaluation: Studies and Methodologies -- Authors List -- Authors Addresses
This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.